In this work, we propose an unsupervised module that combines wavelet-packet transform and k-means++ clustering to extract frequency features and classify patches of medical images. This module produces region labels for each patch-image, bypassing heavy computation and methodological labelling. Our WeCREST model, powered by this module, outperforms CycleGAN in terms of SSIM and PSNR, partly outperforms the supervised pix2pix, but underperforms compared to the state-of-the-art weakly supervised WeCREST. This improvement of the original WeCREST provides new insights into wavelet-based feature extraction and unsupervised region-style classification for medical images.
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